Are GenAI copilots helping us work smarter – or just faster at fixing the wrong problems? Let me introduce you to the concept of failure demand.
The most widespread adoption of GenAI is copilots – Office365 CoPilot and coding assistants. Most evidences suggests they deliver incremental productivity gains for individuals: write a bit more code, draft a doc faster, create a presentation in less time.
But why are you doing those tasks in the first place? This is where the concept of failure demand comes in.
Originally coined by John Seddon, failure demand is the work created when systems, processes, or decisions fail to address root causes. Instead of creating value, you spend time patching over problems that shouldn’t have existed in the first place.
Call centres are a perfect example.
Most call centre demand isn’t value demand (customers seeking products or services). It’s failure demand: caused by unclear communication, broken systems, or unresolved issues.
GenAI might help agents handle calls faster, but the bigger question is why are people calling at all?
The same applies to all knowledge work. Faster coding or document creation only accelerates failure demand if the root issues (e.g. unclear requirements, poor alignment, unnecessary work) – go unaddressed.
Examples:
– Individual speed gains might mask systemic problems, making them harder to spot and fix and reducing the incentive to do so.
– More documents and presentations could bury teams in information, reducing clarity and alignment.
– More code written faster could overwhelm QA teams or create downstream integration issues.
There’s already evidence which suggests this. The 2024 DORA Report (an annual study of engineering team performance) found found AI coding assistants marginally improved individual productivity but correlated with a downward trend in team performance.
The far bigger opportunities lies in asking:
– Why does this work exist?
– Can we eliminate or prevent it?
Unless GenAI helps addressing systemic issues, it risks being a distraction. While it might improve individual productivity, it could hurt overall performance.
Completely agree.
There was a piece in the MIT Sloan Management review this year (https://sloanreview.mit.edu/article/will-large-language-models-really-change-how-work-is-done/) reporting that
“One study suggests that programmers prefer using LLMs … because they provide a better starting point than searching online for existing code to reuse. However, this approach does not improve the success rate of programming tasks. … additional time is required to debug and understand the code the LLM has generated.” (Original study Vaithilingham, Zhang, Glassman “Expectations vs Experience” 2022 CHI Conference on Human Fctors in Computing systems)
History shows that when technology makes something easier or cheaper to do we do more of it. (Word processors etc. have seen an increase in the size of company from 5 to 25 reports (source lost).) So we can expect co-pilot to result in the creation of more text in reports which then take longer to read and may contain errors (same Sloan piece suggests authors are not inclinded to validate LLM results).
But thats OK, because we’ll all have LLM to read the longer reports. Which means English becomes a data interchange format.
And what do human’s learn here?
If anything we make conversation more valuable because we are devaluing documents.
Interesting article. Always refreshing to read things that aren’t all about the hype. Thanks for sharing it Allan 🙂